Technical Field
This invention relates generally to the field of computerized knowledge engineering and expert systems and more particularly to computer systems for managing applied knowledge, decision making, risk assessment, and workflow iteratively amongst several dimensions and contexts.
Background
In many areas of endeavor, one or more bodies of knowledge exist about conditions or circumstances which might affect human lives. For example, in health care, a considerable amount of clinical knowledge is available in electronic form about various diseases and the treatments for each. Similarly, in educational systems significant information exists about intelligence and academic performance indicators and ways to encourage achievement. In commercial marketing, demographics data can be gathered and stored electronically about buyers and their preferences, so that marketing can be done and targeted to prospective buyers with similar characteristics. However, a major problem with such systems is that they are usually limited in scope to one or two dimensions and do not take into account contextual information or other bodies of knowledge that might affect the outcome. They also tend to be limited in their ability to provide plans for actions, assign individuals to carry out the actions, and their ability to manage the workflow and follow-up on the actions when multiple tasks and individuals are involved.
For example, in health care systems, in early computer applications, simple databases were created to store information about diseases and differential diagnosis. An attending physician would have to suspect the existence of one or more diseases and check for those in the database. Now automated expert systems embody the experience of one or more diagnostic physicians. Such an expert system helps the attending physician diagnose an illness, often by asking a number of questions of the attending physician about the patient. Such systems help in diagnosing and treating an individual. However, they are still operating only in the dimensions of diagnosis and immediate treatment. U.S. Pat. No. 5,583,758, to McIlroy et al., describes a health care management system which selects treatment guidelines from a data base of diagnosed conditions, and enables a user to propose alternative treatments, which the system will then compare with the system recommended treatment and submit to another health care provider for review and approval.
McIlroy and similar expert systems assume there is one primary disease in question which needs diagnosis or treatment. However, as is often the case for the elderly, multiple diseases may be present in the same individual. One patient might have diabetes, a heart condition, and a respiratory system problem. The systems that allow a user to describe a patient having multiple ailments often require that the same information be entered redundantly about the patient for each disease. Even systems that might take this into account do not consider the patient's life circumstances and tendencies. An outpatient care plan for an elderly male with multiple diseases may be prone to failure if it relies on self-administration of a complex schedule of medications together with frequent trips to a physical therapy facility across town. This is so especially if the man is recently widowed, lives alone, and is poor. Such an individual may not be motivated or able to follow the medication schedule and may not have the resources or assistance needed to get to the therapist's office.
Yet the same outpatient plan might work for a married man of reasonable means whose wife is both healthy and able to drive him to and from the physical therapist's. Systems which focus only on diagnosis and treatment, even for patients with multiple diseases usually do not take these life circumstance factors into account.
Not only do most systems that make use of knowledge bases have limitations in scope, they also tend to be non-iterative or non-recursive. That is, little or no follow-up is done using the data that was used at first. In the above example, most systems do not have a way to check up or record the fact that the care plan was or was not followed and then plan the next step accordingly.
Another defect of many existing systems is that they are designed for only one or two types of participant-actors or task performers. In health care again, a system may be designed for physicians or pharmacists, but not include activities and analysis for nurses, therapists, aides, home caregivers, and administrators. Thus, the system is not able to reflect all that needs to be done or has been done for a given patient. Such a system might record that a doctor has diagnosed a condition or that the pharmacist has prescribed for it, but the system is not designed to ask a caregiver to give the medication or an administrator to arrange for transportation. At best, some systems will enable nurses or administrators to review the patient records.
Until recently, there had been little impetus to develop more comprehensive systems, since many of the computerized bodies of knowledge were used in industries where fees were charged by service. Health care in many countries used to be primarily fee for service. Health Maintenance Organizations (HMO's) and other health care systems and providers have changed the fee structure in many countries to a flat or fixed fee structure, in which resources must be used as efficiently as possible.
Other corporate structures have also changed recently to more resource-conscious methods of working, as well. In school systems, the tools which allow school administrators to identify high achievers or low achievers are usually not tied to systems which manage resources or interventions. As school budgets come under increased pressure, with fixed per pupil per year budgets, interventions must be done with optimum resource usage in mind. In many corporate structures, the focus on resources also includes a greater emphasis on delegation (where allowed) and teamwork. In commercial businesses, many middle management positions have been eliminated in favor of delegating more responsibility to the employees. Nurse practitioners now often do some of the preliminary fact gathering for physicians in HMO's. This change of focus from results-only to results and best quality use of resources means that many existing computerized or information technology (IT) expert systems or knowledge databases do not adequately address resource usage.
Another stumbling block in the development of more comprehensive systems has been the need for accountability and record keeping in many industries. Health care, again, is a good example. To avoid errors and malpractice claims, most systems need to permanently record diagnoses, prescriptions and actions taken, and insure that the electronic records cannot be changed at a later date. Frequently this is done in a patient database that is separate from most other systems or databases. Records in the patient database usually serve only one or two purposes. First, they permanently record the diagnosis made and care given. Second, they may be used in bill preparation.
Expert systems are usually not linked or related to other systems, since the expert systems were developed for one-dimensional use, such as diagnosis, or for computer application software support. As the name implies, an expert system is usually based on the expertise of one individual or type of individual, such as a diagnostic physician. Expert systems for differential diagnoses (with a few exceptions for occupational diseases) usually do not take into account any other aspects of the patient's life circumstances, since they are not likely to be relevant to a proper diagnosis. Similarly, pharmaceutical expert systems focus on matters such as drug interactions and drug toxicity, but not whether the patient is likely to have a spouse (who can help with the medication schedule) or transportation. U.S. Pat. No. 5,563,805, to Arbuckle et al., describes a network for linking different types of computer software application experts over a network in which each expert has available to him or her some help data on a computer. However, this does not integrate any of the work, knowledge bases or follow-up and is essentially a way of screening or directing a caller to a certain type of human expert.
Expert systems and knowledge bases have also been built using neural network technology, in which elements which are initially connected in a random or hypothetical mix are molded by operational feedback into a pattern that produces better and better results, so that the computer system "learns" in a sense. U.S. Pat. No. 5,622,171, to Asada, et al., 1997, for example, describes a system for differential diagnosis based on clinical and radiological information using artificial neural networks. Here, too, however, the new knowledge that is learned is related to one particular type of expertise, namely the dimension of differential diagnosis.
Actions that are taken based on the use of such expert systems or knowledge bases are also usually limited to the field of expertise in question. Even in the Arbuckle patent, discussed above, the different "experts" were experts in different parts of a single computer application program, using help knowledge about that part of the program, not experts as diverse as a physician and a physical therapist. If the expert system is a diagnostic system, the physician will complete a diagnosis and possibly prescribe treatment. If the expert system is a pharmaceutical one, a drug dosage report and interaction warning might be the resultant action. Similarly, other expert systems or knowledge base systems such as those used in school systems, or manufacturing, or engineering are usually designed to incorporate one particular type of action for performance by someone with one type of skill level.
The shift in emphasis from a fee for service, unlimited resource business model to a fixed budget, managed resource one also means that more emphasis needs to be placed on analyzing the actions and nature of the consumers of the service or resource. Here the Pareto principle or "80/20" rule often comes into play.
In health care organizations for example, it is very likely that only 20 percent (or fewer) of the members use 80 percent (or more) of the resources. One way to address this in the early stages of market penetration by a health care organization is to continue to enroll new members, to bring in new fees and subsidize that 20 percent using the resources. As the market penetration increases, it becomes advisable to merge with other similar organizations in order to reduce administrative overhead costs. When market penetration is optimal, however, new revenue sources are less likely to be found, and traditional cost savings through mergers and acquisitions are less likely, so the subsidies for the high resource users begin to diminish. Another phenomenon also tends to occur as the health care organization matures, namely, its members tend to age and require more health care services.
One way to control costs is by analyzing the services provided to those 20 percent who consume 80 percent of the resources. Unfortunately, most of the installed systems have not been designed to assist in his approach to providing resources more efficiently. Instead, some health care providers analyze existing patient database printouts manually or use statistical analysis software to see if they can find correlations that help in screening or identifying individuals who might be at higher risk for using more resources. Others conduct surveys to try to collect such information. Short surveys may not collect enough data about the individuals to provide useful information. Detailed surveys, on the other hand, are often expensive to compile and analyze and still may not provide useful data.
Very often, the factors that may be most likely to affect resource usage are not captured in any of the databases or surveys. In the example of the widowed elderly male described above, most systems and surveys will not have recorded all the facts about his current life circumstances. His records may show that he is single, but they will probably not indicate that he was recently widowed, is poor, and has no home caregiver or relative nearby to assist him. If the institution has not captured the data about all the factors that may affect outcomes, it is hampered in trying to optimize resources.
Fewer still are the institutions that are able to propose activities they wish to take to minimize unnecessary resource usage by the "20 percent." For the elderly male, again, once these life factors are correlated with a higher risk that he will use more resources, what should the institution do? If this patient is more likely to use ambulances and emergency care facilities and require hospitalization, what steps can the heath care organization take to lower those risks?
Even as some clinicians and care providers develop manual ways to assess these risks and recommend interventions, existing disease management expert or knowledge base systems are not designed to make use of this data. For example, the hypothetical expedient of providing free shuttle bus services to outpatients might have a significant impact on resource usage, but existing systems are usually not able to identify the patients who are most likely to need this. Nor can existing systems create the care plans and workflow that will insure the shuttle bus stops at the elderly male's home every Monday morning at 9 am to take him to the doctor's.
While computers have inspired and enabled the development of expert systems and knowledge bases, they have tended to impose rigid structures on data. For example, one way to collect information about patients is through the use of relational databases. In a relational database there are tables which describe the types of records or files that are included. For example, one table may describe patient name and address records in a predefined format. Another may describe admission records. Another table may list diagnoses. The "relations" between these tables are usually described in a master table which shows how a patient record may be linked to admissions records and diagnoses records. If new kinds of information are to be collected about the patient, new record types must be added or fixed fields must be redefined and the various affected tables must be updated or changed. This is often time consuming and costly for information technology (IT) staff personnel to implement. Thus, most information collected is stored in static formats in pre-defined record or table fields.
In actuality, however, the amount and kinds of data that need to be collected about an individual may vary over time. Using health care again, immediately after a heart attack and bypass surgery, more information about clinical care and treatment needs to be collected while the patient is recovering in hospital. As the patient is ready to be released, information about home circumstances becomes more relevant. Home circumstances may change again if the patient's wife dies while the patient is still recovering at home.
Existing systems do not usually accommodate the ongoing collection and expansion of such information, particularly when unforeseen events such as the wife's death occur. Not only do most systems find it difficult to capture such information, they are also not able to make use of it in changing workflow and action assignments. For example, this patient may now need a shuttle bus or similar service to bring him in for checkups.
It is an object of this invention to provide a system for managing applied knowledge bases in multiple dimensions.
It is another object of the present invention to integrate workflow management with the management of applied knowledge bases.
Still another object of the present invention is simplifying the integration of new domains of knowledge and information into the system.